2tasks in this job
44skills it uses
4tools named
The skills this job leans on most
- 1Designing and versioning data contracts for consumersNeeded in 2 of 2 tasks
- 2Defining measurable outcomes and leading indicatorsNeeded in 1 of 2 tasks
- 3Evaluating product outcomes and metric integrityNeeded in 1 of 2 tasks
ToolsCollibraMicrosoft PurviewMicrosoft ExcelMicrosoft PowerPointMicrosoft Power BIJiraDatabricksSnowflake
All 2 tasks, where people get stuck and all 44 skills
Tasks
- Define and govern a KPI metric contract
- Define a data product's consumers, contract and roadmap and decide its next increment or retirement
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Planning delivery dependencies and the critical path
- Verifying benefits realisation and attribution
All skills
- Defining measurable outcomes and leading indicators
- Designing and versioning data contracts for consumers
- Evaluating product outcomes and metric integrity
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Calculating and verifying KPIs against their specification
- Communicating quantified uncertainty
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Facilitating evidence-led retrospectives and corrective actions
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Making accountable decisions under uncertainty
- Mapping stakeholders' decisions, constraints and needs
- Prioritising work by evidenced value, cost, risk and confidence
- Reading business metrics
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Testing competing explanations with discriminating evidence
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Discovering Requirements and Defining Metrics
Turn stakeholder needs into verifiable requirements and governed metric definitions with owners, grain and acceptance tests.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonManaging Data and AI Products
4tasks in this job
80skills it uses
8tools named
The skills this job leans on most
- 1Designing and versioning data contracts for consumersNeeded in 4 of 4 tasks
- 2Defining measurable outcomes and leading indicatorsNeeded in 2 of 4 tasks
- 3Prioritising work by evidenced value, cost, risk and confidenceNeeded in 2 of 4 tasks
- 4Evaluating product outcomes and metric integrityNeeded in 2 of 4 tasks
- 5Planning delivery dependencies and the critical pathNeeded in 1 of 4 tasks
ToolsCollibraMicrosoft PurviewMicrosoft ExcelMicrosoft PowerPointMicrosoft Power BIJira+ 2 more
All 4 tasks, where people get stuck and all 80 skills
Tasks
- Define and govern a KPI metric contract
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Define a data product's consumers, contract and roadmap and decide its next increment or retirement
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
- Governing shared data definitions
- Calculating and verifying KPIs against their specification
- Specifying BI measure semantics: additivity, aggregation and time
All skills
- Defining measurable outcomes and leading indicators
- Designing and versioning data contracts for consumers
- Evaluating product outcomes and metric integrity
- Planning delivery dependencies and the critical path
- Prioritising work by evidenced value, cost, risk and confidence
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Calculating and verifying KPIs against their specification
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing semantic models for governed analytics
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Governing shared data definitions
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting customer feedback and qualitative evidence
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether external evidence applies to the decision context
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Revising prior decisions when evidence changes
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying BI measure semantics: additivity, aggregation and time
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonManaging Data and AI ProductsDelivering Data and AI Projects with Evidence
5tasks in this job
96skills it uses
7tools named
The skills this job leans on most
- 1Designing and versioning data contracts for consumersNeeded in 5 of 5 tasks
- 2Governing shared data definitionsNeeded in 4 of 5 tasks
- 3Calculating and verifying KPIs against their specificationNeeded in 3 of 5 tasks
- 4Specifying BI measure semantics: additivity, aggregation and timeNeeded in 3 of 5 tasks
- 5Defining measurable outcomes and leading indicatorsNeeded in 2 of 5 tasks
ToolsCollibraMicrosoft PurviewMicrosoft ExcelMicrosoft PowerPointDatabricksJira+ 1 more
All 5 tasks, where people get stuck and all 96 skills
Tasks
- Define and govern a KPI metric contract
- Prioritise and rebalance an investment portfolio against value, capacity and benefit evidence
- Assign data ownership and stewardship and decide a domain's data standards
- Define a data product's consumers, contract and roadmap and decide its next increment or retirement
- Agree a data contract with source and consumer owners and assess the impact of upstream changes
Where people get stuck
- Prioritising work by evidenced value, cost, risk and confidence
- Evaluating product outcomes and metric integrity
All skills
- Calculating and verifying KPIs against their specification
- Defining measurable outcomes and leading indicators
- Designing and versioning data contracts for consumers
- Governing shared data definitions
- Specifying BI measure semantics: additivity, aggregation and time
- Applying data classification and handling rules
- Appraising options on a common incremental basis against a do-nothing baseline
- Assessing data source credibility and fitness for a question
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assigning and operating data ownership and stewardship
- Building cohort and retention analyses with correct denominators
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Comparing options against explicit decision criteria
- Comparing periods consistently
- Controlling disclosure risk in shared data and outputs
- Designing and comparing decision scenarios
- Designing and validating digital analytics event measurement
- Designing automated pipelines and processes that fail safely and recover
- Designing decision rights and dispute routes for data and AI work
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing enforceable data governance policies
- Designing experiments
- Designing semantic models for governed analytics
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing duplicate records
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Escalating unresolved risks with evidence and clear ownership
- Establishing and declaring data grain
- Estimating and challenging causal effects
- Estimating quantities and differences with intervals
- Evaluating product outcomes and metric integrity
- Facilitating evidence-led retrospectives and corrective actions
- Gating releases on evidence with rollback ready
- Giving specific developmental feedback on data and AI work
- Identifying and prioritising critical data elements
- Identifying and quantifying decision downside and exposure
- Identifying keys and testing referential integrity
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Investigating anomalies
- Investigating procurement spend leakage and duplicate commitments
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether external evidence applies to the decision context
- Locating and acting on personal data for individual rights requests
- Maintaining a risk register with owners, triggers and responses
- Making accountable decisions under uncertainty
- Managing master data identities and exceptions
- Mapping personal data processing and flows
- Mapping stakeholders' decisions, constraints and needs
- Monitoring data quality and freshness in production
- Planning delivery dependencies and the critical path
- Presenting and defending evidence-based recommendations to decision-makers
- Prioritising work by evidenced value, cost, risk and confidence
- Profiling data
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reasoning about systems, feedback and second-order effects
- Recognising and containing suspected personal data breaches
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Revising prior decisions when evidence changes
- Sampling and reviewing a team's work against standards
- Setting and reviewing reliability objectives and error budgets for data and AI services
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Testing changes to data services and business systems for business meaning and downstream impact
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Testing, implementing and sustaining measured improvements
- Tracing data lineage upstream and downstream
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying benefits realisation and attribution
- Verifying project progress against accepted outcomes
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Collibra
- Microsoft Purview
- Microsoft Excel
- Microsoft PowerPoint
- Databricks
- Jira
- Snowflake
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonManaging Data and AI ProductsDelivering Data and AI Projects with Evidence
About the role
Accountable for the value and investment of a portfolio of data products and the consistency of their contracts. Manages data product managers and decides which data products are funded or retired. Data judgement sits in reading usage and value evidence across many consumers and keeping definitions consistent between products.
Also called Data Product Portfolio Manager